Meta 的超级智能实验室于周一发布了两款全新的顶尖模型——Muse Wonder 和 Muse Wonder Full，这标志着该公司首次尝试推出前沿模型。
Meta’s Superintelligence Lab released a pair of new state-of-the-art models on Monday, Muse Wonder and Muse Wonder Full, representing the company’s first attempt at a frontier model.
与旨在实现低延迟运行的轻量级模型 Muse Spark 不同，Wonder 系列模型规模更大，据称能力更强，在基准测试中得分更高，并且在编码等复杂任务上的表现也更出色。
Where Muse Spark aimed to be a lightweight model that could perform at low latency, the Wonder models are larger and said to be more capable, with higher benchmark scores and better results on complex tasks like coding.
根据 Meta 的测试（详见模型卡），Wonder 和 Wonder Full 在基准测试中的得分与 Fable 5.1 和 GPT-6 Astra 相当。
According to Meta’s testing (presented in the model card ), Wonder and Wonder Full achieve comparable benchmark scores to Fable 5.1 and GPT-6 Astra.
Meta 在关于这两款模型的博文中写道：“它们共同标志着我们在模型扩展之路上迈出了新的一步，也是向个人超级智能方向实现重大跨越的重要里程碑。”
“Together, they mark the next step on our scaling ladder and a major leap toward personal superintelligence,” Meta’s blog post about the models reads.
其规模也体现在定价上：通过 Meta 模型 API 访问时，Wonder 的输入令牌费用为每百万个 4 美元，输出令牌费用为每百万个 20 美元，成本大致与 GPT-6 Sol 或 Opus 5.5 相当。
That scale is reflected in the pricing: Accessed through the Meta Model API, Wonder will cost $4 per million input tokens and $20 per million output tokens, making it roughly equivalent in cost to GPT-6 Sol or Opus 5.5.
值得注意的是，Wonder Full 将仅对 Muse Coders 开放，不会通过 API 提供服务。两款模型还将用于驱动 Muse 智能体。
Notably, Wonder Full will only be available to Muse Coders and will not be served through the API. Both models will also be used to power the Muse agent.
鉴于 Meta 对开发能够长期执行任务的个人助理的兴趣，该公司特别强调了新模型在保留和综合用户提供的信息方面的能力，这一特性被 Meta 称为“主动记忆”。为了提升记忆能力，Meta 重点介绍了 MemoryBench 基准测试，该测试旨在挑战持续学习和记忆构建框架。根据公司的测试，Muse Wonder 在 MemoryBench 上的得分现已超过 Fable、Opus 和 Astra。
In keeping with Meta’s interest in developing personal assistants that can perform tasks over a long period, the company placed particular emphasis on the new models’ ability to retain and synthesize information given by the user, a property Meta calls “active memory.”To achieve better recall, Meta highlighted the MemoryBench benchmark which challenges continual learning and memory construction frameworks. According to the company’s testing, Muse Wonder now scores higher on MemoryBench than Fable, Opus, or Astra.
研究科学家 Kavya Kopparapu 在发布时配套的一段视频中表示：“我们投入了巨大的努力，确保模型能够跨越数日、数周、数月乃至更长时间，始终记住对您而言重要的信息。”
“We put a tremendous amount of effort into making sure your model can remember what’s important to you over days, weeks, months, and beyond,” said research scientist Kavya Kopparapu in a video accompanying the launch.
新模型是Scale AI创始人亚历山大·王去年八月重组Meta的AI工作以来首次发布的前沿模型。此后，Meta发布了其轻量级Muse Spark模型和Muse代理，但避免了OpenAI和Anthropic以之闻名的那种最先进模型发布。
The new models are the first frontier model release since Scale AI founder Alexandr Wang restructured Meta’s AI efforts in August of last year Since then, Meta has released its lightweight Muse Spark model and Muse agent but has avoided the kind of state-of-the-art model releases OpenAI and Anthropic are known for.
根据该帖子，这些模型在位于俄亥俄州中部的Meta普罗米修斯数据中心进行训练，该数据中心今年早些时候投入运行。
According to the post, the models were trained at Meta’s Prometheus data center in central Ohio which came online earlier this year.